A deep learning approach to solar radio flux forecasting

Emma Stevenson, Victor Rodriguez-Fernandez, Edmondo Minisci, David Camacho

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
23 Downloads (Pure)


The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variable.
Original languageEnglish
Pages (from-to)595-606
Number of pages12
JournalActa Astronautica
Early online date26 Feb 2022
Publication statusPublished - 30 Apr 2022


  • astronautics
  • solar radio flux
  • space weather
  • deep learning
  • time series forecasting
  • ensemble
  • spacecraft dynamics
  • atmospheric drag


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